智能小区可转移柔性负荷实时需求响应策略
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摘要
近年来随着我国电力体制改革的深化与智能电网技术的发展,需求响应开始逐步应用于居民用户负荷侧。针对智能小区的居民柔性可转移负荷,考虑居民负荷用电不确定性,提出一种适用于负荷聚合商的居民可转移柔性负荷实时需求响应调度策略。该策略利用2阶段随机优化模型与基于Copula的蒙特卡洛模拟相结合的方法对各负荷设备进行每小时1次调度决策的实时滚动优化,从而实现居民可转移柔性负荷的实时随机调度。该策略可在不影响用户满意度情况下有效降低用户用电成本,减小负荷峰值及峰谷差,使智能电网条件下的居民可转移柔性负荷能够有效参与到需求响应中。
With the deepening of China's power system reform and the development of smart grid technology in recent years, demand response has been gradually implemented on demand side. In the light of the shiftable residential flexible load under the circumstance of smart residential community, considering the uncertainty of residential load, a real-time demand response scheme is presented for the shiftable residential flexible load incorporating its power consumption uncertainties, which can be applied by load aggregator. A twostage stochastic optimization model is integrated with Copula-based Monte Carlo simulation to formulate the real-time rolling optimal scheduling for different load appliances, which fulfills the real-time stochastic scheduling of shiftable residential flexible load. The proposed strategy can significantly decrease the power consumption cost, the peak load, and the peak-valley difference of the residential load without sacrificing the residents comfort. The shiftable load of smart gird residential flexible load can efficiently participate in the demand response program.
With the deepening of China's power system reform and the development of smart grid technology in recent years, demand response has been gradually implemented on demand side. In the light of the shiftable residential flexible load under the circumstance of smart residential community, considering the uncertainty of residential load, a real-time demand response scheme is presented for the shiftable residential flexible load incorporating its power consumption uncertainties, which can be applied by load aggregator. A twostage stochastic optimization model is integrated with Copula-based Monte Carlo simulation to formulate the real-time rolling optimal scheduling for different load appliances, which fulfills the real-time stochastic scheduling of shiftable residential flexible load. The proposed strategy can significantly decrease the power consumption cost, the peak load, and the peak-valley difference of the residential load without sacrificing the residents comfort. The shiftable load of smart gird residential flexible load can efficiently participate in the demand response program.
引文
[1]蔡德华,陈柏熹,程乐峰,等.实施需求侧管理对提高发电系统可靠性的影响探究[J].电力系统保护与控制,2015, 43(10):51-56.CAI Dehua, CHEN Baixi, CHENG Lefeng, et al.Effective study about the implementation of demand side management on improving reliability of generation system[J]. Power System Protection and Control, 2015, 43(10):51-56.
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[8]付蔚,敬章浩,罗志勇,等.基于分时电价的智能家电控制方案[J].电网技术,2015, 39(3):717-723.FU Wei, JING Zhanghao, LUO Zhiyong, et al. Smart home appliances control scheme based on TOU price[J].Power grid technology, 2015. 39(3):717-723.
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[10]汤奕,鲁针针,伏祥运.居民主动负荷促进分布式电源消纳的需求响应策略[J].电力系统自动化,2015, 39(24):49-55.TANG Yi, LU Zhenzhen, FU Xiangyun, et al. Demand response strategies for promoting consumption of distributed power generation with residential active loads[J]. Automation of Electric Power Systems, 2015, 39(24):49-55.
[11]陈世喆,李天然,严鹏,等.智能居民住宅负荷控制策略下的经济调度[J].电气技术,2016, 17(11):25-30+35.CHEN Shizhe, LI Tianran, YAN Peng, et al. Economic dispatch under intelligent residential load control strategy[J]. Electrical Engineering, 2016, 17(11):25-30+35.
[12]史林军,史江峰,杨启航,等.基于分时电价的家庭智能用电设备的运行优化[J].电力系统保护与控制,2018,46(24):88-95.SHI Linjun,SHI Jiangfeng, YANG Qihang,et al. Optimal scheduling of intelligent household electrical appliances based on time-of-use power price[J]. Power System Protection and Control,2018,46(24):88-95.
[13]严俊,严凤.峰谷分时电价背景下的居民电动汽车有序充放电策略[J].电力系统保护与控制,2018,46(15):127-134.YAN Jun, YAN Feng. An orderly charging and discharging strategy for residential electric vehicles based on peakvalley electricity tariffs[J]. Power System Protection and Control,2018,46(15):127-134.
[14]余晓玲,余晓婷,韩晓娟.基于思维进化算法的电动汽车有序充电控制策略[J].电力工程技术,2017, 36(6):58-62.YU Xiaoling, YU Xiaoting, HAN Xiaojuan. A coordinated charging strategy for pev charging stations based on mind evolutionary algorithm[J]. Electric Power Engineering Technology, 2017, 36(6):58-62.
[15]张军,韩华春,原增泉.基于两级充电管理系统的电动汽车智能充电控制系统研究[J].电力工程技术,2017,36(5):86-92.ZHANG Jun, HAN Huachun, YUAN Zengquan. Smart charging control electrical vehicles based on two-level charge management system[J]. Electric Power Engineering Technology, 2017, 36(5):86-92.
[16]闫志杰,张蕊萍,董海鹰,等.基于需求响应的电动汽车充放电电价与时段研究[J].电力系统保护与控制,2018, 46(15):16-22.YAN Zhijie, ZHANG Ruiping, DONG Haiying, et al.Price and period research of electric vehicles charging and discharging based on demand response[J]. Power System Protection and Control, 2018, 46(15):16-22.
[17]陆俊,彭文昊,朱炎平,等.基于粒子校正优化的智能小区需求响应调度策略[J].电网技术,2017, 41(07):2370-2377.LU Jun, PENG Wenhao, ZHU Yanping, et al. A scheduling strategy based on particle correction for intelligent power demand response[J]. Power System Technology, 2017, 41(07):2370-2377.
[18] CHAN K W, LUO X. Real-time scheduling of electric vehicles charging in low-voltage residential distribution systems to minimise power losses and improve voltage profile[J]. IET Generation, Transmission&Distribution,2014, 8(3):516-529.
[19] KANG S J, PARK J, OH K-Y, et al. Scheduling-based real time energy flow control strategy for building energy management system[J]. Energy and Buildings, 2014, 75:239-248.
[20]张禹森,孔祥玉,孙博伟,等.基于电力需求响应的多时间尺度家庭能量管理优化策略[J].电网技术,2018,42(06):1811-1819.ZHANG Yusen, KONG Xiangyu, SUN Bowei, et al.Multi-time scale home energy management strategy based on electricity demand response[J]. Power System Technology, 2018, 42(06):1811-1819.
[21]孙毅,裴俊亦,景栋盛.考虑用户行为不确定性的智能家电控制策略[J].电力系统保护与控制,2018, 46(17):109-117.SUN Yi, PEI Junyi, JING Dongsheng. Smart home appliance control strategy considering user behavior uncertainty[J]. Power System Protection and Control,2018, 46(17):109-117.
[22] ABDULAAL A, ASFOUR S. A linear optimization based controller method for real-time load shifting in industrial and commercial buildings[J]. Energy and Buildings,2016, 110:269-283.
[23] YI P, DONG X, IWAYEMI A, et al. Real-time opportunistic scheduling for residential demand response[J]. IEEE Transactions on Smart Grid, 2013, 4(1):227-234.
[24] BINA M T, AHMADI D. Stochastic modeling for the next day domestic demand response applications[J]. IEEE Transactions on Power Systems, 2015, 30(6):2880-2893.
[25] SILVERMAN B W. Density estimation:For statistics and data analysis[M]. London:Chapman and Hall, 1986.
[26] RAYENS B, NELSEN R B. An introduction to copulas[J].Technometrics, 2000, 42(3):317.
[27] DUPACOVA J, GROWE-KUSKA N, ROMISCH W.Scenario reduction in stochastic programming-An approach using probability metrics[J]. Mathematical Programming, 2003, 95(3):493-511.
[2]王蓓蓓.面向智能电网的用户需求响应特性和能力研究综述[J].中国电机工程学报,2014, 34(22):3654-3663.WANG Beibei. Research on consumers'response characteristics and ability under smart grid:a literatures survey[J]. Proceedings of the CSEE, 2014, 34(22):3654-3663.
[3] SIAO P, SARNO D. Assessing the benefits of residential demand response in a real time distribution energy market[J]. Applied Energy, 2016, 161:533-551.
[4]何德卫,杨威,陈皓勇,等.电力市场环境下需求侧响应相关问题的探讨[J].智慧电力,2018, 46(6):41-48.HE Dewei, YANG Wei, CHEN Haoyong, et al.Discussion on demand response under electricity market environment[J]. Smart Power, 2018, 46(6):41-48.
[5]王珂,姚建国,姚良忠,等.电力柔性负荷调度研究综述[J].电力系统自动化,2014, 38(20):127-135.WANG Ke, YAO Jianguo, YAO Liangzhong, et al.Survey of research on flexible loads scheduling technologies[J]. Automation of Electric Power Systems,2014, 38(20):127-135.
[6]赵鸿图,周京阳,于尔铿.支撑高效需求响应的高级量测体系[J].电网技术,2010, 34(9):13-20.ZHAO Hongtu, ZHOU Jingyang, YU Erkeng. Advanced metering infrastructure supporting effective demand response[J]. Power System Technology, 2010, 34(9):13-20.
[7]高赐威,李倩玉,李慧星,等.基于负荷聚合商业务的需求响应资源整合方法与运营机制[J].电力系统自动化,2013, 37(17):78-86.GAO Ciwei, LI Qianyu, LI Huixing, et al. Methodology and operation mechanism of demand response resources integration based on load aggregator[J]. Automation of Electric Power Systems, 2013, 37(17):78-86.
[8]付蔚,敬章浩,罗志勇,等.基于分时电价的智能家电控制方案[J].电网技术,2015, 39(3):717-723.FU Wei, JING Zhanghao, LUO Zhiyong, et al. Smart home appliances control scheme based on TOU price[J].Power grid technology, 2015. 39(3):717-723.
[9]曹筱欧,王冬,周静,等.含储能及新能源发电的居民侧需求响应[J].信息与控制,2017, 46(3):379-384.CAO Xiao'ou, WANG Dong, ZHOU Jing, et al.Residential demand side response considering energy storages and renewable energy sourced generators[J].Information and Control, 2017, 46(3):379-384.
[10]汤奕,鲁针针,伏祥运.居民主动负荷促进分布式电源消纳的需求响应策略[J].电力系统自动化,2015, 39(24):49-55.TANG Yi, LU Zhenzhen, FU Xiangyun, et al. Demand response strategies for promoting consumption of distributed power generation with residential active loads[J]. Automation of Electric Power Systems, 2015, 39(24):49-55.
[11]陈世喆,李天然,严鹏,等.智能居民住宅负荷控制策略下的经济调度[J].电气技术,2016, 17(11):25-30+35.CHEN Shizhe, LI Tianran, YAN Peng, et al. Economic dispatch under intelligent residential load control strategy[J]. Electrical Engineering, 2016, 17(11):25-30+35.
[12]史林军,史江峰,杨启航,等.基于分时电价的家庭智能用电设备的运行优化[J].电力系统保护与控制,2018,46(24):88-95.SHI Linjun,SHI Jiangfeng, YANG Qihang,et al. Optimal scheduling of intelligent household electrical appliances based on time-of-use power price[J]. Power System Protection and Control,2018,46(24):88-95.
[13]严俊,严凤.峰谷分时电价背景下的居民电动汽车有序充放电策略[J].电力系统保护与控制,2018,46(15):127-134.YAN Jun, YAN Feng. An orderly charging and discharging strategy for residential electric vehicles based on peakvalley electricity tariffs[J]. Power System Protection and Control,2018,46(15):127-134.
[14]余晓玲,余晓婷,韩晓娟.基于思维进化算法的电动汽车有序充电控制策略[J].电力工程技术,2017, 36(6):58-62.YU Xiaoling, YU Xiaoting, HAN Xiaojuan. A coordinated charging strategy for pev charging stations based on mind evolutionary algorithm[J]. Electric Power Engineering Technology, 2017, 36(6):58-62.
[15]张军,韩华春,原增泉.基于两级充电管理系统的电动汽车智能充电控制系统研究[J].电力工程技术,2017,36(5):86-92.ZHANG Jun, HAN Huachun, YUAN Zengquan. Smart charging control electrical vehicles based on two-level charge management system[J]. Electric Power Engineering Technology, 2017, 36(5):86-92.
[16]闫志杰,张蕊萍,董海鹰,等.基于需求响应的电动汽车充放电电价与时段研究[J].电力系统保护与控制,2018, 46(15):16-22.YAN Zhijie, ZHANG Ruiping, DONG Haiying, et al.Price and period research of electric vehicles charging and discharging based on demand response[J]. Power System Protection and Control, 2018, 46(15):16-22.
[17]陆俊,彭文昊,朱炎平,等.基于粒子校正优化的智能小区需求响应调度策略[J].电网技术,2017, 41(07):2370-2377.LU Jun, PENG Wenhao, ZHU Yanping, et al. A scheduling strategy based on particle correction for intelligent power demand response[J]. Power System Technology, 2017, 41(07):2370-2377.
[18] CHAN K W, LUO X. Real-time scheduling of electric vehicles charging in low-voltage residential distribution systems to minimise power losses and improve voltage profile[J]. IET Generation, Transmission&Distribution,2014, 8(3):516-529.
[19] KANG S J, PARK J, OH K-Y, et al. Scheduling-based real time energy flow control strategy for building energy management system[J]. Energy and Buildings, 2014, 75:239-248.
[20]张禹森,孔祥玉,孙博伟,等.基于电力需求响应的多时间尺度家庭能量管理优化策略[J].电网技术,2018,42(06):1811-1819.ZHANG Yusen, KONG Xiangyu, SUN Bowei, et al.Multi-time scale home energy management strategy based on electricity demand response[J]. Power System Technology, 2018, 42(06):1811-1819.
[21]孙毅,裴俊亦,景栋盛.考虑用户行为不确定性的智能家电控制策略[J].电力系统保护与控制,2018, 46(17):109-117.SUN Yi, PEI Junyi, JING Dongsheng. Smart home appliance control strategy considering user behavior uncertainty[J]. Power System Protection and Control,2018, 46(17):109-117.
[22] ABDULAAL A, ASFOUR S. A linear optimization based controller method for real-time load shifting in industrial and commercial buildings[J]. Energy and Buildings,2016, 110:269-283.
[23] YI P, DONG X, IWAYEMI A, et al. Real-time opportunistic scheduling for residential demand response[J]. IEEE Transactions on Smart Grid, 2013, 4(1):227-234.
[24] BINA M T, AHMADI D. Stochastic modeling for the next day domestic demand response applications[J]. IEEE Transactions on Power Systems, 2015, 30(6):2880-2893.
[25] SILVERMAN B W. Density estimation:For statistics and data analysis[M]. London:Chapman and Hall, 1986.
[26] RAYENS B, NELSEN R B. An introduction to copulas[J].Technometrics, 2000, 42(3):317.
[27] DUPACOVA J, GROWE-KUSKA N, ROMISCH W.Scenario reduction in stochastic programming-An approach using probability metrics[J]. Mathematical Programming, 2003, 95(3):493-511.